A/B Testing Runtime Calculator
If possible, a test should not last too long – 1-2 weeks are ideal. However, depending on the effort and the test scenario, a duration of 4 weeks can also make sense.
A / B Test Runtime Test
In order to be able to calculate the test duration, the following metrics are required for the 3 points described above:
- How much traffic (number of visitors) is there monthly / daily To calculate a test duration, you need the traffic number for the website to be tested. In order to then specifically start a test with a certain number of variants, one should first be aware of how many visitors are available in total to be tested.
- How much traffic should be included or excluded? Then you can determine how many visitors would be ideal for a test, or whether you want to exclude users from a test, for example.
- How many variants should be tested? We always assume 2 variants (reference vs. variation), but with more traffic it is also possible to test further variations.
Auszug aus unseren Referenzen
1. Collect Ideas & Hypotheses
2. Prioritize Ideas & Hypotheses
3. Implement and Execute Campaigns
4. Evaluation & Analysis
Our Services in A/B Testing & Personalization
1. Target Definition
– Selecting the tools that fit your needs best (e.g. Google Optimize, Optimizely)
2. Side Variations
3. Execution of the A/B test & analysis of the results
– Evaluation of the results for the derivation of new measures for your website
Our A/B Testing & Personalization Team
- Full Stack Entwickler
- 6 years of experience in data analytics & market research
- Adobe Analytics certified expert
- 10+ Jahre experience in Digital Analytics, MarTech & Tech SEO
- Google Analytics & Adobe Analytics expert
- 6 Jahre expericience in Digital Analytics, MarTech & Digital Marketing
- Google Analytics expert
Discover more about A/B Testing & Personalization
Further questions about A/B Testing & Personalization
Which elements are tested during A/B testing?
Is A/B testing only performed on websites?
In addition to websites, A/B testing can also be performed for emails, PPC ads, and CTA buttons.
What is a null hypothesis?
How often should I run A/B tests?
Client vs. Server Side Testing?
- Client-side: commonly used to optimize conversion rates in marketing or funnel, for example by creating page variations directly on the users’ browser.
- Server-side: when you need to test more in depth in relation to the visual changes, such as products (features) or experience for engagement, retention and more.
Do A/B tests have negative effects on SEO?
How many users do I need for trust-worthy testing?
- The conversion rate of our control variation (variation A)
- Minimum difference between the conversion values of variations
- Confidence level
- Statistical “Power”
For a sample calculation please use our runtime calculator on this page.